Bayesian imputation optimizes bias-variance tradeoff in time-series data.
problem Look-ahead bias in imputation of missing time-series data.
method Wasserstein interpolation for Bayesian posterior consensus distribution.
result Optimal control of look-ahead bias and variance in imputation.
Look-Ahead-Bench evaluates financial LLMs for lookahead bias, revealing significant differences in model performance.
problem Measuring and mitigating lookahead bias in financial LLMs.
method Standardized benchmark evaluating model behavior in practical financial scenarios, analyzing performance decay across market regimes.
result Standard LLMs exhibit significant lookahead bias, while Pitinf models show improved generalization and reasoning abilities.
Bayesian imputation optimizes bias-variance trade-off in time-series data.
problem Look-ahead bias in imputation of missing time-series data.
method Bayesian consensus posterior that fuses multiple posteriors to optimize bias and variance trade-off.
result Benefit of imputation for portfolio allocation with missing returns demonstrated.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
The least squares Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz (2001) is widely used for pricing Bermudan options. The LSM estimator contains undesirable look-ahead bias, and the conventional technique of avoiding it requires additional simulation paths. We present the leave-one-out LSM (LOOLSM) algor…
A new property fixes look-ahead bias in backtesting and trading pipelines.
problem Fixing look-ahead bias in backtesting and trading pipelines.
method Developed a pipeline calculus separating availability from reference time, and a type-and-effect system for the value-independent fragment.
result The check scales linearly and catches all leaks, including those missed by differential and tiling detectors.
Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the "look-ahead benchmark bias," when t…
EB improves asset pricing by mining large strategies without lookahead bias.
problem Lack of unbiased asset pricing models with out-of-sample performance.
method Empirical Bayes applied to 136,000 long-short strategies.
result EB provides unbiased predictions with transparent intuition.
Anonymization reduces economic signal extraction from financial texts.
problem Reducing meaningful economic signals from financial texts due to anonymization.
method Analyzed the impact of anonymization on textual understanding and economic signal extraction.
result Information loss due to anonymization is severe and pervasive, outweighing its benefits in certain financial applications.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
Financial LLMs need explicit bias consideration to avoid invalid results.
problem Finance-specific biases inflate performance and contaminate backtests.
method Identified five recurring biases and proposed a Structural Validity Framework.
result Explicit bias consideration is necessary for valid deployment claims.
Study shows optimal RL with transition look-ahead is NP-hard for ℓ≥2.
problem Optimal reinforcement learning with transition look-ahead is computationally hard.
method Proved NP-hardness for ℓ≥2 using linear programming. result There is a precise boundary between tractable and intractable cases for RL with look-ahead.
New acquisition functions improve Bernoulli LSE.
problem Efficiently estimating regions where a Bernoulli function is above or below a threshold.
method Developed new look-ahead acquisition functions for Gaussian process classification models.
result Demonstrated clear benefits of new acquisition functions on benchmark and real-world tasks.
MLMC boosts Bayesian optimization's look-ahead efficiency.
problem Efficiently computing nested expectations in Bayesian optimization.
method Multilevel Monte Carlo (MLMC) for nested operations.
result MLMC achieves MC convergence rate for nested operations, improving BO performance.
New method uses neural tangent kernel for efficient active learning.
problem Efficiently approximating deep learning's look-ahead selection criteria.
method Approximates retraining with neural tangent kernel for active learning.
result Approximation works asymptotically and enables sequential active learning.
La-MAML improves fast online continual learning with a look-ahead approach.
problem Fast online continual learning with limited model capacity.
method Optimisation-based meta-learning with look-ahead and episodic memory.
result Superior performance on visual classification benchmarks.
Motivated by the widespread use of temporal-difference (TD-) and Q-learning algorithms in reinforcement learning, this paper studies a class of biased stochastic approximation (SA) procedures under a mild "ergodic-like" assumption on the underlying stochastic noise sequence. Building upon a carefully designed multistep…
Bayesian method optimizes rescheduling for multipurpose batch processes with incomplete look-ahead information.
problem Optimizing rescheduling for multipurpose batch processes under incomplete look-ahead information.
method Proposes a Bayesian dynamic scheduling method that learns from disturbances and updates schedules online.
result Achieves statistically better long-term costs and system nervousness compared to existing periodic rescheduling strategies.
New screening rules improve lasso model fitting efficiency.
problem Efficiently solving high-dimensional lasso problems.
method Look-ahead screening rules to discard predictors.
result Look-ahead screening rules outperform existing methods.
NeuralCut learns to select cutting planes by looking ahead, outperforming traditional methods.
problem Selecting effective cutting planes for MILP optimization.
method Imitation learning on a lookahead expert to train a neural network for cut selection.
result NeuralCut outperforms standard baselines in cut selection for MILP benchmarks.
Look-ahead reasoning helps predict strategic user behavior on learning platforms.
problem Optimization criteria on learning platforms do not reflect users' priorities.
method Formalized level-k thinking and contrasted collective and selfish behavior.
result Coordination benefits users but does not offer higher-level reasoning advantages in the long run.
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for…
BALLAST optimizes Lagrangian observer placement for ocean vector fields.
problem Optimizing Lagrangian observer placement for time-dependent ocean vector fields.
method Bayesian active learning with look-ahead amendment for sea-drifter trajectories using a physics-informed spatio-temporal Gaussian process surrogate model.
result Noticeable benefits of BALLAST-aided observer placement strategies on synthetic and high-fidelity ocean models.
Study when to replace machine learning models with new data.
problem When to switch machine learning models with new data sources.
method Unified economic and statistical framework linking learning-curve dynamics, data-acquisition, and retraining costs.
result Look-ahead sequential method outperforms other methods and approaches optimal value.
We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of futu…
Study non-parametric value function estimation from a single path.
problem Estimating value function from a single trajectory in Markov reward processes.
method Kernel-based multi-step temporal difference (TD) estimates, including K-step look-ahead TD and TD(λ). result Non-asymptotic guarantees for TD estimates, capturing interactions between mixing time and model mis-specification.
CLQT benchmarks LLM portfolio managers by evaluating their decision-making process, not just returns.
problem Most benchmarks rank LLMs by returns, ignoring their decision-making process and potential for look-ahead leakage.
method CLQT reframes evaluation as diagnosis, using a closed-loop, cost-aware, strategy-consistent environment with a five-stage cycle.
result CLQT provides a durable map of agent competencies and limitations, separating outcome from process.
Regularizes predictions to encourage beneficial user actions.
problem Learning models affect user actions, but provide no guarantees.
method Introduces look-ahead regularization to anticipate and encourage beneficial actions.
result Effective in promoting beneficial user actions in real and synthetic data.
ASRI index detects crypto market risks with high precision and lead time.
problem Detecting systemic risks in cryptocurrency markets.
method Four weighted sub-indices (Stablecoin, DeFi, Contagion, Regulatory) validated against historical crises.
result ASRI detects significant abnormal signals with high statistical significance and lead time.
Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.
problem Best arm identification (BAI) problem with limitations of the original KG algorithm.
method Follows the one-step look ahead of KG but chooses the measurement that maximizes the probability of selecting the best arm.
result Improved knowledge gradient (iKG) is asymptotically optimal and easier to extend to variant BAI problems.
GPT model shows promise in financial market predictions.
problem Challenges in predicting stock market movements due to noisy data.
method Comparing GPT model with BERT model using Federal Reserve Beige Book data.
result GPT model has look-ahead bias, traditional models still perform better.
We study an exploration method for model-free RL that generalizes the counter-based exploration bonus methods and takes into account long term exploratory value of actions rather than a single step look-ahead. We propose a model-free RL method that modifies Delayed Q-learning and utilizes the long-term exploration bonu…
Adaptive optimal control of nonlinear dynamic systems with deterministic and known dynamics under a known undiscounted infinite-horizon cost function is investigated. Policy iteration scheme initiated using a stabilizing initial control is analyzed in solving the problem. The convergence of the iterations and the optim…
Improved algorithm for optimal stopping problems reduces runtime.
problem Optimal stopping problems with infinite time horizon and random discounting.
method Flexible forward improvement iteration with a variable look-ahead distance.
result The new algorithm converges and can significantly reduce runtime.
ePF improves PF for ITS by balancing exploration and exploitation, outperforming baselines.
problem Premature exploitation in PF leads to suboptimal solutions under constrained budgets.
method Integrates Entropic Annealing and Look-ahead Modulation to preserve diversity and evaluate potential.
result Significant improvement in task reward (up to 50% relative) on math benchmarks.
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
problem Predicting stock returns with AI, focusing on identifying top winners.
method Deployed a state-of-the-art LLM to autonomously search the web for stock attractiveness, avoiding look-ahead bias.
result AI can generate alpha by identifying top winners, but returns are highly concentrated.
Improves graph-based active learning for non-Gaussian models.
problem Efficiently selecting data points for labeling in graph-based semi-supervised learning.
method Approximates non-Gaussian distributions, introduces rank-one update and model change acquisition function.
result Enhanced active learning for graph-based SSL under non-Gaussian models.
This study uses HMM and RL to dynamically allocate equities, Treasuries, and gold based on market regimes.
problem Developing a dynamic portfolio allocation strategy for different market conditions.
method Characterizes market regimes using Markov switching models and HMM, then applies RL for allocation decisions.
result RL-based allocation outperforms passive strategies, providing lower drawdowns and higher Sharpe ratios.
Proposes a comprehensive framework for financial product lead recommendations using graph representation learning and link prediction.
problem Challenges in surface lead recommendations for financial products due to changing market scenarios and difficulty in capturing holder's mindset.
method Bi-partite graph representation of financial holders and funds, GraphSage model for learning representations, link prediction model for ranking recommendations.
result The proposed graph ML solution outperforms baseline by 42%, 22%, and 14% in hit rate for top-k recommendations (50, 100, 200) and 18%, 19%, and 18% on unseen holders.
We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training process. To allow for a smooth dynamic growth of the network, we introduce a look-ahead initialization that outperforms the random initializa…
This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.
problem Greedy testing strategies miss dormant virus areas, leading to inefficient use of testing resources.
method Develops efficient learning strategies based on policy iteration and look-ahead rules for a sequential learning-based resource allocation problem.
result Shows that the testing problem can be effectively managed using a partially observable MDP approach.
Research predicts cryptocurrency staking rewards with high accuracy.
problem Predicting cryptocurrency staking rewards.
method Two predictive methodologies: sliding-window average and linear regression models.
result ETH staking rewards can be forecasted with RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively.
The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms. Recent attempts to improve SGD can be broadly categorized into two approaches: (1) adaptive learning rate schemes, such as AdaGrad and Adam, and (2) accelerated schemes, such as heavy-ball an…
MarketSenseAI system outperforms passive benchmarks by 25.2% on S&P 500, adding value over random selection.
problem Identifying alpha in stock recommendations from multi-agent LLM systems.
method Deployed multi-agent LLM equity system generating live signals, combining four specialist agents into a synthesis agent.
result Strong-buy equal-weight portfolio on S&P 500 earns +2.18%/month, significantly outperforming passive benchmarks.
Bayesian deep learning improves deep learning's capabilities across diverse settings.
problem Overlooked metrics, tasks, and data types in deep learning.
method Revisits strengths of Bayesian deep learning and addresses challenges.
result Bayesian deep learning can elevate deep learning's capabilities across diverse settings.
Paper extends knowledge gradient for preferential BO, overcoming computational challenges.
problem Extending knowledge gradient to preferential BO with pairwise comparisons.
method Derive exact and analytical knowledge gradient for preferential BO.
result Exact knowledge gradient outperforms existing acquisition functions on benchmark problems.
Probabilistic Bisection Algorithm performs root finding based on knowledge acquired from noisy oracle responses. We consider the generalized PBA setting (G-PBA) where the statistical distribution of the oracle is unknown and location-dependent, so that model inference and Bayesian knowledge updating must be performed s…
Research shows bias in machine learning can be due to algorithmic flaws, not just data.
problem Underestimation bias in machine learning algorithms.
method Initial research to understand factors contributing to bias in classification algorithms.
result Regularization methods to address overfitting can also accentuate bias.